docs: def extend_to_data(self, Y):
          """Build transition matrix from new data to the graph
  
          Creates a transition matrix such that `Y` can be approximated by
          a linear combination of samples in `self.data`. Any
          transformation of `self.data` can be trivially applied to `Y` by
          performing
  
          `transform_Y = self.interpolate(transform, transitions)`
  
          Parameters
          ----------
  
          Y: array-like, [n_samples_y, n_dimensions]
              new data for which an affinity matrix is calculated
              to the existing data. `n_features` must match
              either the ambient or PCA dimensions
  
          Returns
          -------
  
          transitions : array-like, shape=[n_samples_y, self.data.shape[0]]
              Transition matrix from `Y` to `self.data`
          """
          Y = self._check_extension_shape(Y)
          kernel = self.build_kernel_to_data(Y)
          transitions = normalize(kernel, norm='l1', axis=1)
          return transitions